Explaining the striking difference in twist-stretch coupling between DNA and RNA: A comparative molecular dynamics analysis

Double stranded helical DNA and RNA are flexible molecules that can undergo global conformational fluctuations. Their bending, twisting and stretching deformabilities are of similar magnitude. However, recent single-molecule experiments revealed a striking qualitative difference indicating an opposite sign for the twist-stretch couplings of dsDNA and dsRNA [Lipfert et al. 2014. Proc. Natl. Acad. Sci. U.S.A. 111, 15408] that is not explained by existing models. Employing unconstrained Molecular Dynamics (MD) simulations we are able to reproduce the qualitatively different twist-stretch coupling for dsDNA and dsRNA in semi-quantitative agreement with experiment. Similar results are also found in simulations that include an external torque to induce over-or unwinding of DNA and RNA. Detailed analysis of the helical deformations coupled to twist indicate that the interplay of helical rise, base pair inclination and displacement from the helix axis upon twist changes are responsible for the different twist-stretch correlations. Overwinding of RNA results in more compact conformations with a narrower major groove and consequently reduced helical extension. Overwinding of DNA decreases the size of the minor groove and the resulting positive base pair inclination leads to a slender and more extended helical structure

Explaining the striking difference in twist-stretch coupling between DNA and RNA: A comparative molecular dynamics analysis

Double stranded helical DNA and RNA are flexible molecules that can undergo global conformational fluctuations. Their bending, twisting and stretching deformabilities are of similar magnitude. However, recent single-molecule experiments revealed a striking qualitative difference indicating an opposite sign for the twist-stretch couplings of dsDNA and dsRNA [Lipfert et al. 2014. Proc. Natl. Acad. Sci. U.S.A. 111, 15408] that is not explained by existing models. Employing unconstrained Molecular Dynamics (MD) simulations we are able to reproduce the qualitatively different twist-stretch coupling for dsDNA and dsRNA in semi-quantitative agreement with experiment. Similar results are also found in simulations that include an external torque to induce over-or unwinding of DNA and RNA. Detailed analysis of the helical deformations coupled to twist indicate that the interplay of helical rise, base pair inclination and displacement from the helix axis upon twist changes are responsible for the different twist-stretch correlations. Overwinding of RNA results in more compact conformations with a narrower major groove and consequently reduced helical extension. Overwinding of DNA decreases the size of the minor groove and the resulting positive base pair inclination leads to a slender and more extended helical structure

Boron-incorporating silicon nanocrystals embedded in SiO2: absende of free carriers vs. B-induced defects

Boron (B) doping of silicon nanocrystals requires the incorporation of a B-atom on a lattice site of the quantum dot and its ionization at room temperature. In case of successful B-doping the majority carriers (holes) should quench the photoluminescence of Si nanocrystals via non-radiative Auger recombination. In addition, the holes should allow for a non-transient electrical current. However, on the bottom end of the nanoscale, both substitutional incorporation and ionization are subject to significant increase in their respective energies due to confinement and size effects. Nevertheless, successful B-doping of Si nanocrystals was reported for certain structural conditions. Here, we investigate B-doping for small, well-dispersed Si nanocrystals with low and moderate B-concentrations. While small amounts of B-atoms are incorporated into these nanocrystals, they hardly affect their optical or electrical properties. If the B-concentration exceeds ~1 at%, the luminescence quantum yield is significantly quenched, whereas electrical measurements do not reveal free carriers. This observation suggests a photoluminescence quenching mechanism based on B-induced defect states. By means of density functional theory calculations, we prove that B creates multiple states in the bandgap of Si and SiO2. We conclude that non-percolated ultra-small Si nanocrystals cannot be efficiently B-doped

Optical emission from SiO2-embedded silicon nanocrystals: a high pressure Raman and photoluminescence study

We investigate the optical properties of high-quality Si nanocrystals (NCs)/SiO2 multilayers under high hydrostatic pressure with Raman scattering and photoluminescence (PL) measurements. The aim of our study is to shed light on the origin of the optical emission of the Si NCs/SiO2. The Si NCs were produced by chemical-vapor deposition of Si-rich oxynitride (SRON)/SiO2 multilayers with 5- and 4-nm SRON layer thicknesses on fused silica substrates and subsequent annealing at 1150 °C, which resulted in the precipitation of Si NCswith an average size of 4.1 and 3.3 nm, respectively. From the pressure dependence of the Raman spectra we extract a phonon pressure coefficient of 8.5 ± 0.3 cm−1/GPa in both samples, notably higher than that of bulk Si (5.1 cm−1/GPa). This result is ascribed to a strong pressure amplification effect due to the larger compressibility of the SiO2 matrix. In turn, the PL spectra exhibit two markedly different contributions: a higher-energy band that redshifts with pressure, and a lower-energy band which barely depends on pressure and which can be attributed to defect-related emission. The pressure coefficients of the higher-energy contribution are (−27 ± 6) and (−35 ± 8) meV/GPa for the Si NCs with a size of 4.1 and 3.3 nm, respectively. These values are sizably higher than those of bulk Si (−14 meV/GPa). When the pressure amplification effect observed by Raman scattering is incorporated into the analysis of the PL spectra, it can be concluded that the pressure behavior of the high-energy PL band is consistent with that of the indirect transition of Si and, therefore, with the quantum-confined model for the emission of the Si NCs

Data science for environmental health

Ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our ecosystems as well as their ecosystem functions. The relationships between drivers, stress and ecosystem functions in ecosystems are complex, multi- faceted and often non-linear and yet environmental managers, decision makers and politicians need to be able to make rapid decisions that are data-driven and based on short- and long-term monitoring information, complex modeling and analysis approaches. A huge number of long-standing and standardized ecosystem health approaches like the essential variables already exist and are increasingly integrating remote-sensing based monitoring approaches [1-2]. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. This presentation therefore discusses the requirements for using Data Science as a bridge between complex and multidimensional Big Data for environmental health. It became apparent that no existing monitoring approach, technique, model or platform is sufficient on its own to monitor, model, forecast or assess vegetation health and its resilience. In order to advance the development of a multi-source ecosystem health monitoring network, we argue that in order to gain a better understanding of ecosystem health in our complex world it would be conducive to implement the concepts of Data Science with the components: (i) digitalization, (ii) standardization with metadata management adhering to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, (iii) Semantic Web, (iv) proof, trust and uncertainties, (v) complex tools for Data Science analysis and (vi) easy tools for scientists, data managers and stakeholders for decision-making support [3-4]. REFERENCES: 1.Lausch, A., Bannehr, L., Beckmann, M., Boehm, C., Feilhauer, H., Hacker, J.M., Heurich, M., Jung, A., Klenke, R., Neumann, C., Pause, M., Rocchini, D., Schaepman, M.E.; Schmidtlein, S., Schulz, K., Selsam, P., Settele, J., Skidmore, A.K., Cord, A.F., 2016. Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives. Ecol. Indic. 70, 317–339. doi:10.1016/j.ecolind.2016.06.022. 2.Lausch, A., Erasmi, S., Douglas, J., King, Magdon, P., Heurich, M., 2016. Understanding forest health with remote sensing - Part I - A review of spectral traits, processes and remote sensing characteristics. Remote Sens. 8, 1029; doi:10.3390/rs8121029. 3.Lausch, A.; Bastian O.; Klotz, S.; Leitão, P. J.; Jung, A.; Rocchini, D.; Schaepman, M.E.; Skidmore, A.K.; Tischendorf, L.; Knapp, S. 2018. Understanding and assessing vegetation health by in-situ species and remote sensing approaches. Methods Ecol. Evol. 00, 1–11. doi:10.1111/2041-210X.13025. 4.Lausch, A., Borg, E., Bumberger, J., Dietrich, P., Heurich, M., Huth, A., Jung, A., Klenke, R., Knapp, S., Mollenhauer, H., Paasche, H., Paulheim, H., Pause, P., Schweitzer, C., Schmulius, C., Settele, J., Skidmore, A.K.,, Wegmann, M., Zacharias, S., Kirsten, T.; Schaepman, M.E., 2018. Understanding forest health with remote sensing -Part III - Requirements for a scalable multi-source forest health monitoring network based on Data Science approaches. (Remote Sens., in review)